Monte Carlo analysis as a tool to incorporate variation on experimental data in predictive microbiology Original Research Article

Until now, most of the mathematical models used in predictive microbiology are deterministic, i.e. their outcome is a point estimate for the microbial load at a certain time instant. For more advanced exploitation of predictive microbiology in the context of hazard analysis and critical control points and risk analysis studies, stochastic models should be developed. Such models predict a probability mass function for the microbial load at a certain time instant. The objective of this paper is to illustrate methodologically how to generate, starting from the experimental observations and a deterministic growth model, probability density functions for (i) the model parameters and (ii) the predictions as a function of time, by using Monte Carlo analysis. A normal distribution over the experimental data was considered. This probabilistic approach, incorporating experimental variation, is applied to experimental growth data of Escherichia coli K12 and Listeria innocua ATCC 33090.